Electricity/Solar Update

A look at the latest Jedlovec House eletricity usage and solar production from PPL and Enphase.

Load packages

library(tidyverse)
library(lubridate)
library(hms)
library(readxl)
library(onlineBcp)

Load PPL data

col_datatypes <- c('numeric','numeric','date','text',rep('numeric',99))

hourly1 <- read_excel("Hourly Usage 220326 to 220624.xlsx", col_types = col_datatypes)

Warning: Expecting numeric in A277 / R277C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'

hourly2 <- read_excel("Hourly Usage 220625 to 230623.xlsx", col_types = col_datatypes)

Warning: Expecting numeric in A1096 / R1096C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'

hourly3 <- read_excel("PPL 230624 to 240509.xlsx", col_types = col_datatypes)

Warning: Expecting numeric in A967 / R967C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'

#hourly1
#hourly2
#hourly3

ppl_15mins <- bind_rows(hourly1,list(hourly2,hourly3))

ppl_15mins %>% arrange(desc(Date), `Read Type`)

NA
NA

Transform PPL Data

hourly_ppl_pivot <- ppl_15mins %>% 
  rename(date = Date) %>% 
  pivot_longer(!c("Account Number", "Meter Number", date, "Read Type", Min, Max, Total), names_to = "time", values_to = "kWh") 

rename(ppl_15mins, date = Date)

hourly_ppl_pivot <- hourly_ppl_pivot %>% 
  mutate(time = parse_time(time, '%H:%M %p'), month = month(date, label=TRUE), year = year(date), yday = yday(date), wday = wday(date, label=TRUE))

(hourly_ppl_net <- hourly_ppl_pivot %>% 
  filter(`Read Type` == "kWh Net"))

Load Enphase data

import <- read_csv("enphase_history_230701.csv")

Rows: 42432 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

update <- read_csv("hourly_generation_230625_240510.csv")

Rows: 30816 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

update <- update %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  filter(`Date/Time` >= as.POSIXct('07/01/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) )

import <- import %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) 

full_hist <- rbind(import,update)

full_hist %>% arrange(desc(`Date/Time`))

(hourly_production <- full_hist %>% 
  rename(datetime = `Date/Time`, energy_produced_Wh = `Energy Produced (Wh)`) %>% 
  mutate(datetime = as.POSIXct(datetime,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  mutate(date = date(datetime), time = as.hms(format(datetime, format = "%H:%M:%S")), month = month(datetime, label=TRUE), year = year(datetime), day = day(datetime), yday = yday(datetime), monthday = format(datetime, "%m-%d"), wday = wday(datetime, label=TRUE), equinox_day = (yday + 10) %% 365, equinox_group = floor((equinox_day+15)/30)*30)
)

NA

Spot-check Enphase Should see lifetime production by day and by hour

ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
  geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_point()

Net + Produced = Consumed

# hourly_ppl_net <- hourly_ppl_net %>% mutate(date = as_date(date))

#hourly_ppl_net %>% arrange(desc(date))
#hourly_production %>% arrange(desc(date))

(hourly_electricity <- hourly_ppl_net %>% 
    inner_join(hourly_production, by = join_by(date,time))  %>% 
    mutate(consumed_kWh = kWh + energy_produced_Wh/1000, produced_kWh = energy_produced_Wh/1000)  %>% 
    rename(net_kWh = kWh) %>% 
    select(datetime, date, time, net_kWh, produced_kWh, consumed_kWh) %>%
    arrange(date))

NA

Calculate production for first year of solar panels, second year, etc.

hourly_electricity <- hourly_electricity %>% 
  mutate(solar_year = factor(case_when(
                        datetime < as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 0,
                        datetime >= as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone())  &
                          datetime < as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 1,
                        datetime >= as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone())  &
                          datetime < as.POSIXct('04/16/2024 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 2,
                        TRUE ~ 3)
         )) %>% 
  mutate(yday = yday(date))

hourly_electricity %>% 
  group_by(solar_year) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

NA

Interesting! Produced kWh went down by 5%, but consumed kWh went down by 10% despite the fact that we got an electric car! Let’s explore that further.

daily_electricity <- hourly_electricity %>% 
  group_by(solar_year, yday) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.

ggplot(daily_electricity, aes(yday, consumed_kWh, group=solar_year, color=solar_year)) + 
  geom_point() +
  geom_smooth(span=0.3) +
  scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
  theme(axis.text.x = element_text(angle = 90)) 

Daily totals

daily_electricity <- hourly_electricity %>% 
  mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) 
         ) %>% 
  group_by(date, solar_day) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

`summarise()` has grouped output by 'date'. You can override using the `.groups` argument.

ggplot(daily_electricity, aes(date, consumed_kWh)) + 
  geom_point() +
  #scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
  theme(axis.text.x = element_text(angle = 90)) 

NA
NA

Look how our electricity consumption got much less predictable, more uneven, after we purchased an EV and installed a level 2 charger in late January/early February 2023!

Also, a couple of outlier high points in Dec 2022 before we got the EV… hmm.

We probably need to filter out the EV charging and compare that separately.

Average consumption based on time of day

#hourly_electricity %>% summarize(min_date = min(date), max_date = max(date))

electricity_by_time <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  #filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>% 
  group_by(time, solar_year) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(time)

`summarise()` has grouped output by 'time'. You can override using the `.groups` argument.

electricity_by_time

ggplot(electricity_by_time, aes(x=time, y=consumed_kWh, group=solar_year, color=solar_year)) +
  geom_point() 

  #labs(colour="",x="Time of Day",y="Electricity Consumption (kWh)")+
  #scale_color_manual(values = c("red","black","green")) +
  #ggtitle("Home Electricity in 15-Minute Intervals (Since April 15, 2022)")

Wow, look how much less was used during the day in year 2!!! Like a 50% reduction!

Is this due to milder weather?

Let’s look at it by month:

#electricity_by_month <- 
hourly_electricity %>% 
  mutate(month = month(date)) %>% 
  group_by(solar_year, month) %>% 
  summarize(consumed_kWh = mean(consumed_kWh)) %>% 
  arrange(month, solar_year) %>% 
  pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
  arrange(solar_year)

`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.

#Look at limited window during day (no EV charging)
 
# hourly_electricity %>% 
#   filter(time == '12:00:00') %>% 
#   #filter(time > as.POSIXct('04:00:00',format="%H:%M:$s",tz=Sys.timezone()) ) %>%  #& time < as.POSIXct('18:00',format="%H:%M",tz=Sys.timezone())) %>% 
#   mutate(month = month(date)) %>% 
#   group_by(solar_year, month) %>% 
#   summarize(consumed_kWh = mean(consumed_kWh)) %>% 
#   arrange(month, solar_year) %>% 
#   pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
#   arrange(solar_year)

Look at specific months

electricity_by_month_time <- hourly_electricity %>% 
  mutate(month = as_factor(month(date))) %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  #Sample month
  filter(month == 6) %>% 
  group_by(solar_year, month, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(solar_year, month, time)

`summarise()` has grouped output by 'solar_year', 'month'. You can override using the `.groups` argument.

ggplot(electricity_by_month_time, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

Example day with EV charging

sample_day <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  filter(yday == 161) %>% 
  group_by(date, solar_year, yday, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(date, solar_year, yday, time)

`summarise()` has grouped output by 'date', 'solar_year', 'yday'. You can override using the `.groups` argument.

ggplot(sample_day, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

Ok, let’s detect and remove EV charging sessions.

Bayesian change point example

# library(onlineBcp)
x <- c(rnorm(10, 2.6, 0.2), rnorm(1, 2.6/2, 0.2) + rnorm(1, .2, .15), rnorm(86, .2, .15))
bcp <- online_cp(x)
summary(bcp)

[1] "Change points"

[1] "Segments"
     begin end      mean        SD  LL of CI UL of CI
[1,]     1  11 2.4159210 0.2461319 2.2938538 2.537988
[2,]    12  97 0.2128337 0.1483191 0.1865265 0.239141

bcp

$x
 [1]  2.3720905715  2.4691440621  2.6071250448  2.5352472423  2.2877005354  2.3857597432  2.7419924694  2.3295656691
 [9]  2.6664633812  2.3582885866  1.8217537652  0.0930585696  0.3230388724  0.0197739677  0.2570762359  0.1881634498
[17]  0.0792020254  0.1498961292  0.3642266363  0.3817626425  0.1885297288  0.3544041698  0.2879920055  0.0953605082
[25]  0.0446978082  0.0004123957  0.1959692210  0.0693755586  0.4736216346  0.1488568573  0.2188121995  0.0812417378
[33]  0.3080128352  0.4839328145  0.3567281316  0.0095019304  0.4530073963  0.3672416208  0.3294527060  0.1982238893
[41]  0.2996424154  0.1570348120  0.2920901580  0.2199320912  0.0822511010  0.4977626932  0.2455526621  0.4334779288
[49]  0.2376445401  0.2338382842 -0.0402372143  0.2225591660  0.1183037401  0.3194898662  0.1986949765  0.1917810490
[57]  0.0807082047  0.2332076344 -0.1524857206  0.0582340793  0.0665107477  0.0714005187 -0.0557819132  0.2851434290
[65]  0.3313943646  0.2103237245 -0.1036259056  0.1887882159  0.2929783093  0.0780226903 -0.1688743454  0.4017997097
[73]  0.3595800053  0.1888407383  0.1824374153  0.1784951176  0.1609317339  0.2098454959  0.3643482946  0.3487322792
[81]  0.2841377275  0.2780164560  0.0203063376  0.0949771645  0.2602946798  0.3359891574  0.1761905070  0.5173409818
[89]  0.1462872854  0.4356854056  0.1592010947  0.3728770785  0.2836787830  0.1263039884  0.4396970634  0.0967142059
[97]  0.2336566264

$max_p
           [,1] [,2] [,3]
 [1,] 0.9577733    0    0
 [2,] 0.9383008    0    1
 [3,] 0.9289778    0    2
 [4,] 0.9231639    0    3
 [5,] 0.9241976    0    4
 [6,] 0.9287806    0    5
 [7,] 0.9299975    0    6
 [8,] 0.9358318    0    7
 [9,] 0.9381648    0    8
[10,] 0.9254240    0    9
[11,] 0.4559258    0   10
[12,] 0.6111704   11    0
[13,] 0.8090391   11    1
[14,] 0.7996688   11    2
[15,] 0.7973283   11    3
[16,] 0.7991989   11    4
[17,] 0.8056386   11    5
[18,] 0.8089248   11    6
[19,] 0.8134703   11    7
[20,] 0.8252870   11    8
[21,] 0.8317779   11    9
[22,] 0.8411875   11   10
[23,] 0.8487528   11   11
[24,] 0.8538773   11   12
[25,] 0.8569608   11   13
[26,] 0.8680025   11   14
[27,] 0.8731902   11   15
[28,] 0.8663527   11   16
[29,] 0.8785399   11   17
[30,] 0.8858196   11   18
[31,] 0.8890118   11   19
[32,] 0.8923711   11   20
[33,] 0.8820230   11   21
[34,] 0.8895051   11   22
[35,] 0.8903923   11   23
[36,] 0.8879874   11   24
[37,] 0.8942148   11   25
[38,] 0.8993633   11   26
[39,] 0.9044254   11   27
[40,] 0.9066541   11   28
[41,] 0.9088015   11   29
[42,] 0.9107461   11   30
[43,] 0.9130828   11   31
[44,] 0.9113711   11   32
[45,] 0.9002217   11   33
[46,] 0.9116611   11   34
[47,] 0.9077451   11   35
[48,] 0.9147518   11   36
[49,] 0.9175931   11   37
[50,] 0.9030689   11   38
[51,] 0.9158742   11   39
[52,] 0.9176531   11   40
[53,] 0.9198118   11   41
[54,] 0.9222592   11   42
[55,] 0.9235917   11   43
[56,] 0.9208759   11   44
[57,] 0.9248626   11   45
[58,] 0.8835135   11   46
[59,] 0.9079202   11   47
[60,] 0.9164723   11   48
[61,] 0.9203609   11   49
[62,] 0.9085255   11   50
[63,] 0.9224407   11   51
[64,] 0.9244629   11   52
[65,] 0.9285093   11   53
[66,] 0.9042838   11   54
[67,] 0.9240923   11   55
[68,] 0.9277552   11   56
[69,] 0.9275764   11   57
[70,] 0.8848931   11   58
[71,] 0.9081096   11   59
[72,] 0.9189911   11   60
[73,] 0.9283150   11   61
[74,] 0.9312630   11   62
[75,] 0.9325588   11   63
[76,] 0.9331760   11   64
[77,] 0.9340624   11   65
[78,] 0.9292055   11   66
[79,] 0.9290267   11   67
[80,] 0.9322733   11   68
[81,] 0.9335335   11   69
[82,] 0.9274462   11   70
[83,] 0.9314169   11   71
[84,] 0.9346385   11   72
[85,] 0.9326805   11   73
[86,] 0.9357910   11   74
[87,] 0.9075830   11   75
[88,] 0.9290786   11   76
[89,] 0.9214965   11   77
[90,] 0.9325243   11   78
[91,] 0.9300501   11   79
[92,] 0.9343632   11   80
[93,] 0.9350886   11   81
[94,] 0.9243810   11   82
[95,] 0.9313401   11   83
[96,] 0.9362280   11   84
[97,] 0.0000000    0    0

$parameters
theta alpha  beta th_cp 
  0.9   1.0   1.0   0.5 

$series_length
[1] 97

$result
NULL

attr(,"class")
[1] "BayesCP"

Test on electricity consumption data

# library(onlineBcp)

#Select a sample to test
sample <- hourly_electricity %>% 
  mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) ) %>%
  filter(solar_year == 2) %>% 
  filter(yday < 40 & yday > 35)

ggplot(sample, aes(datetime, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

min_time <- pull(sample %>% summarize(min_time = min(datetime)))

sample <- sample %>% 
  mutate(time_x = interval(min_time,datetime)/minutes(15)+1)

#Filter data to feed to model
x <- sample %>%  
  ungroup() %>% 
  select(consumed_kWh)

x <- x$consumed_kWh

#Run online Bayesian Change Point model 
#"Online" means in progress, not retroactive
bcp <- online_cp(x)

summary(bcp)

[1] "Change points"

[1] "Segments"
      begin end      mean         SD  LL of CI  UL of CI
 [1,]     1  22 0.3472727 0.16365416 0.2898818 0.4046636
 [2,]    23  71 0.3963061 0.67936742 0.2366690 0.5559433
 [3,]    72  86 2.6233333 0.28189833 2.5036113 2.7430554
 [4,]    87 118 0.3384375 0.19605972 0.2814289 0.3954461
 [5,]   119 131 1.6671538 0.69094740 1.3519434 1.9823643
 [6,]   132 204 0.4050959 0.23258679 0.3603193 0.4498725
 [7,]   205 226 1.2519091 0.87005845 0.9467935 1.5570246
 [8,]   227 266 0.1373750 0.03542973 0.1281606 0.1465894
 [9,]   267 279 2.5607692 0.44376160 2.3583250 2.7632134
[10,]   280 320 0.6154390 0.70612095 0.4340486 0.7968295
[11,]   321 384 0.2414844 0.21893980 0.1964689 0.2864999

plot(summary(bcp))

[1] "Change points"

[1] "Segments"
      begin end      mean         SD  LL of CI  UL of CI
 [1,]     1  22 0.3472727 0.16365416 0.2898818 0.4046636
 [2,]    23  71 0.3963061 0.67936742 0.2366690 0.5559433
 [3,]    72  86 2.6233333 0.28189833 2.5036113 2.7430554
 [4,]    87 118 0.3384375 0.19605972 0.2814289 0.3954461
 [5,]   119 131 1.6671538 0.69094740 1.3519434 1.9823643
 [6,]   132 204 0.4050959 0.23258679 0.3603193 0.4498725
 [7,]   205 226 1.2519091 0.87005845 0.9467935 1.5570246
 [8,]   227 266 0.1373750 0.03542973 0.1281606 0.1465894
 [9,]   267 279 2.5607692 0.44376160 2.3583250 2.7632134
[10,]   280 320 0.6154390 0.70612095 0.4340486 0.7968295
[11,]   321 384 0.2414844 0.21893980 0.1964689 0.2864999

Not too bad! It picks up the EV charging sessions, but it also picks up some other, presumably HVAC-related consumption changes. It looks like I should be able to weed these out easily.

#Select a sample to test
sample <- hourly_electricity %>% 
  mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) ) %>%
  filter(solar_year == 2)

ggplot(sample, aes(datetime, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

min_time <- pull(sample %>% summarize(min_time = min(datetime)))

sample <- sample %>% 
  mutate(time_x = interval(min_time,datetime)/minutes(15)+1)

#Filter data to feed to model
x <- sample %>%  
  ungroup() %>% 
  select(consumed_kWh)

x <- x$consumed_kWh

#Run online Bayesian Change Point model 
#"Online" means in progress, not retroactive
bcp <- online_cp(x)

summary(bcp)

[1] "Change points"

[1] "Segments"
       begin   end       mean         SD   LL of CI   UL of CI
  [1,]     1   183 0.15867213 0.08151452 0.14876069 0.16858357
  [2,]   184   201 2.54833333 0.18683903 2.47589664 2.62077003
  [3,]   202   255 0.21370370 0.13612198 0.18323467 0.24417273
  [4,]   256   280 0.98676000 0.85564446 0.70527802 1.26824198
  [5,]   281   310 0.09933333 0.01595972 0.09454050 0.10412616
  [6,]   311   317 1.39928571 0.58181002 1.03757661 1.76099482
  [7,]   318   407 0.19588889 0.11159210 0.17654074 0.21523704
  [8,]   408   419 0.60500000 0.27843067 0.47279323 0.73720677
  [9,]   420   465 0.13995652 0.08194794 0.12008247 0.15983057
 [10,]   466   470 0.81600000 0.10968136 0.73531829 0.89668171
 [11,]   471   525 0.12314545 0.05444005 0.11107109 0.13521982
 [12,]   526   547 0.54072727 0.35733620 0.41541519 0.66603935
 [13,]   548   663 0.14043103 0.08869638 0.12688525 0.15397682
 [14,]   664   671 0.77875000 0.07972049 0.73238906 0.82511094
 [15,]   672   739 0.19141176 0.16167936 0.15916194 0.22366159
 [16,]   740   758 1.01136842 0.47855804 0.83078201 1.19195484
 [17,]   759   799 0.28295122 0.15253933 0.24376646 0.32213598
 [18,]   800   836 0.24978378 0.25941603 0.17963451 0.31993306
 [19,]   837   887 0.15256863 0.12176408 0.12452325 0.18061401
 [20,]   888   889 1.63700000 0.22203153 1.37875798 1.89524202
 [21,]   890  1004 0.30826957 0.26276249 0.26796616 0.34857297
 [22,]  1005  1276 0.17872059 0.12311218 0.16644213 0.19099905
 [23,]  1277  1313 0.14602703 0.17472510 0.09877922 0.19327484
 [24,]  1314  1333 0.83575000 0.64251405 0.59943304 1.07206696
 [25,]  1334  1353 2.52600000 0.15367087 2.46947978 2.58252022
 [26,]  1354  1411 0.15462069 0.19762234 0.11193824 0.19730314
 [27,]  1412  1420 0.81455556 0.32066654 0.63873905 0.99037206
 [28,]  1421  1470 0.16828000 0.06658432 0.15279133 0.18376867
 [29,]  1471  1559 0.31550562 0.25288347 0.27141434 0.35959690
 [30,]  1560  1564 1.17880000 0.19822386 1.03298635 1.32461365
 [31,]  1565  1614 0.14046000 0.07016459 0.12413849 0.15678151
 [32,]  1615  1624 1.20980000 0.08634530 1.16488763 1.25471237
 [33,]  1625  1686 0.18277419 0.14634021 0.15220422 0.21334417
 [34,]  1687  1698 3.15425000 0.49666051 2.91842151 3.39007849
 [35,]  1699  1854 0.15433974 0.06694095 0.14552404 0.16315544
 [36,]  1855  1861 0.84342857 0.21794790 0.70793118 0.97892596
 [37,]  1862  1908 0.09882979 0.08260654 0.07901028 0.11864929
 [38,]  1909  1926 2.59166667 0.16059632 2.52940416 2.65392917
 [39,]  1927  2101 0.15522286 0.17138946 0.13391242 0.17653329
 [40,]  2102  2200 0.17367677 0.17937310 0.14402388 0.20332965
 [41,]  2201  2218 2.46388889 0.34700634 2.32935602 2.59842176
 [42,]  2219  2264 0.18486957 0.07748623 0.16607757 0.20366156
 [43,]  2265  2476 0.19737264 0.16586218 0.17863535 0.21610993
 [44,]  2477  2479 0.75833333 0.45885219 0.32258128 1.19408538
 [45,]  2480  2495 2.76906250 0.35950326 2.62122994 2.91689506
 [46,]  2496  2572 0.13181818 0.05257044 0.12196393 0.14167243
 [47,]  2573  2594 0.47481818 0.25175189 0.38653283 0.56310353
 [48,]  2595  2677 0.14198795 0.09708150 0.12446026 0.15951564
 [49,]  2678  2683 0.84000000 0.21071308 0.69850434 0.98149566
 [50,]  2684  2759 0.13947368 0.06365333 0.12746372 0.15148364
 [51,]  2760  2779 3.07070000 0.77736005 2.78478662 3.35661338
 [52,]  2780  3060 0.12311388 0.06383092 0.11685055 0.12937721
 [53,]  3061  3081 2.59571429 0.44914999 2.43449797 2.75693061
 [54,]  3082  3147 0.13696970 0.06288108 0.12423832 0.14970107
 [55,]  3148  3172 0.24856000 0.18372082 0.18812123 0.30899877
 [56,]  3173  3524 0.09061648 0.04654004 0.08653627 0.09469669
 [57,]  3525  3638 0.20732456 0.17125908 0.18094130 0.23370782
 [58,]  3639  3640 1.27500000 0.37476659 0.83911379 1.71088621
 [59,]  3641  3732 0.17995652 0.12108910 0.15919121 0.20072183
 [60,]  3733  3751 2.60526316 0.13226104 2.55535375 2.65517256
 [61,]  3752  3878 0.09667717 0.04120196 0.09066344 0.10269089
 [62,]  3879  3895 0.56470588 0.29951435 0.44521894 0.68419282
 [63,]  3896  4020 0.17004800 0.12667222 0.15141196 0.18868404
 [64,]  4021  4034 2.63500000 0.12882606 2.57836734 2.69163266
 [65,]  4035  4201 0.20934132 0.15551561 0.18954689 0.22913575
 [66,]  4202  4218 3.13858824 0.60423765 2.89753633 3.37964014
 [67,]  4219  4290 0.16494444 0.10834763 0.14394149 0.18594740
 [68,]  4291  4312 0.59354545 0.35545045 0.46889468 0.71819623
 [69,]  4313  4404 0.16091304 0.09404718 0.14478510 0.17704099
 [70,]  4405  4608 0.25860784 0.20945656 0.23448624 0.28272945
 [71,]  4609  4623 2.61266667 0.45051505 2.42133320 2.80400013
 [72,]  4624  4697 0.17281081 0.12154662 0.14956984 0.19605178
 [73,]  4698  4704 0.91428571 0.11222851 0.84451367 0.98405776
 [74,]  4705  4787 0.12692771 0.11129425 0.10683396 0.14702146
 [75,]  4788  4793 0.77833333 0.16154463 0.66985471 0.88681195
 [76,]  4794  4888 0.12975789 0.07625402 0.11688939 0.14262640
 [77,]  4889  4907 2.49736842 0.43878421 2.33179087 2.66294597
 [78,]  4908  5214 0.13712378 0.08533592 0.12911272 0.14513484
 [79,]  5215  5228 2.41135714 0.49351387 2.19440567 2.62830862
 [80,]  5229  5362 0.23662687 0.18332709 0.21057724 0.26267649
 [81,]  5363  5377 2.77400000 0.34652973 2.62682904 2.92117096
 [82,]  5378  5464 0.21665517 0.22068165 0.17773867 0.25557168
 [83,]  5465  5651 0.17728342 0.11530101 0.16341460 0.19115224
 [84,]  5652  5668 2.57411765 0.26923639 2.46670966 2.68152563
 [85,]  5669  5741 0.13509589 0.08354526 0.11901213 0.15117965
 [86,]  5742  5748 1.17614286 0.12781683 1.09667961 1.25560610
 [87,]  5749  6096 0.17443103 0.15804380 0.16049577 0.18836630
 [88,]  6097  6112 2.60718750 0.45955823 2.41821100 2.79616400
 [89,]  6113  6124 0.29750000 0.20276027 0.20122368 0.39377632
 [90,]  6125  6131 2.76257143 0.97367703 2.15724009 3.36790277
 [91,]  6132  6231 0.24848000 0.25669557 0.20625734 0.29070266
 [92,]  6232  6308 0.21520779 0.12653378 0.19148923 0.23892636
 [93,]  6309  6511 0.19472414 0.17713057 0.17427511 0.21517317
 [94,]  6512  6525 2.48407143 0.27672159 2.36242306 2.60571980
 [95,]  6526  6671 0.14185616 0.08498146 0.13028772 0.15342461
 [96,]  6672  6673 0.92100000 0.22910260 0.65453371 1.18746629
 [97,]  6674  6879 0.27940291 0.23138322 0.25288584 0.30591998
 [98,]  6880  6900 2.72452381 0.68929207 2.47711173 2.97193589
 [99,]  6901  7075 0.21150857 0.14219340 0.19382835 0.22918879
[100,]  7076  7095 0.52810000 0.34191826 0.40234231 0.65385769
[101,]  7096  7153 0.19832759 0.12769857 0.17074727 0.22590791
[102,]  7154  7179 0.59700000 0.26742042 0.51073490 0.68326510
[103,]  7180  7280 0.25075248 0.19670305 0.21855827 0.28294668
[104,]  7281  7298 2.83583333 0.19686730 2.75950872 2.91215794
[105,]  7299  7550 0.24308730 0.18568744 0.22384710 0.26232750
[106,]  7551  7563 1.10323077 0.43770579 0.90354925 1.30291229
[107,]  7564  7581 2.75872222 0.75105877 2.46753995 3.04990449
[108,]  7582  7913 0.33763253 0.19658596 0.31988611 0.35537895
[109,]  7914  8044 0.38840458 0.28637675 0.34724894 0.42956022
[110,]  8045  8054 0.72430000 0.23386608 0.60265493 0.84594507
[111,]  8055  8253 0.29678392 0.22912889 0.27006736 0.32350048
[112,]  8254  8278 2.59120000 0.29066476 2.49557980 2.68682020
[113,]  8279  8542 0.35060985 0.24308582 0.32600136 0.37521834
[114,]  8543  8557 2.57466667 0.41889083 2.39676399 2.75256934
[115,]  8558  8620 0.61955556 0.44157080 0.52804792 0.71106319
[116,]  8621  8801 0.31927624 0.21471628 0.29302482 0.34552767
[117,]  8802  8819 0.44194444 0.27913743 0.33372406 0.55016483
[118,]  8820  8833 2.71357143 0.18002594 2.63443101 2.79271185
[119,]  8834  9006 0.38552023 0.36266991 0.34016622 0.43087424
[120,]  9007  9024 2.85411111 0.24699366 2.75835273 2.94986950
[121,]  9025  9120 0.50219792 0.39500832 0.43588504 0.56851080
[122,]  9121  9394 0.44655474 0.30289382 0.41645642 0.47665307
[123,]  9395  9416 2.67545455 0.27109526 2.58038578 2.77052331
[124,]  9417  9555 0.29738129 0.19514767 0.27015533 0.32460726
[125,]  9556  9636 0.41034568 0.31565975 0.35265522 0.46803613
[126,]  9637  9651 2.56346667 0.59115128 2.31240508 2.81452825
[127,]  9652  9872 0.33284615 0.26946999 0.30303069 0.36266161
[128,]  9873  9888 2.66875000 0.28710590 2.55068820 2.78681180
[129,]  9889 10028 0.36028571 0.25706952 0.32454907 0.39602236
[130,] 10029 10165 0.43079562 0.35885920 0.38036538 0.48122586
[131,] 10166 10230 0.22975385 0.22577014 0.18369245 0.27581524
[132,] 10231 10239 2.54944444 0.65648993 2.18950116 2.90938773
[133,] 10240 10251 0.39150000 0.11319693 0.33775089 0.44524911
[134,] 10252 10253 2.40100000 0.66326616 1.62956365 3.17243635
[135,] 10254 10268 0.36380000 0.22700824 0.26738974 0.46021026
[136,] 10269 10284 2.63562500 0.05303694 2.61381550 2.65743450
[137,] 10285 10342 0.18908621 0.15608166 0.15537571 0.22279670
[138,] 10343 10353 0.79100000 0.26893977 0.65762146 0.92437854
[139,] 10354 10454 0.26540594 0.17848794 0.23619299 0.29461889
[140,] 10455 10469 2.47533333 0.51804394 2.25532039 2.69534627
[141,] 10470 10556 0.21736782 0.17195937 0.18704333 0.24769230
[142,] 10557 10564 0.83000000 0.09471763 0.77491757 0.88508243
[143,] 10565 10733 0.23657396 0.16448399 0.21576227 0.25738566
[144,] 10734 10751 2.71350000 0.26413148 2.61109735 2.81590265
[145,] 10752 11007 0.24485938 0.19501390 0.22481129 0.26490746
[146,] 11008 11099 0.34401087 0.26032564 0.29936819 0.38865355
[147,] 11100 11113 0.62950000 0.35320854 0.47422754 0.78477246
[148,] 11114 11134 2.76833333 1.03796514 2.39576977 3.14089690
[149,] 11135 11475 0.19822287 0.15181241 0.18470036 0.21174539
[150,] 11476 11814 0.22865192 0.16220743 0.21416092 0.24314291
[151,] 11815 12283 0.25126013 0.25484719 0.23190390 0.27061635
[152,] 12284 12548 0.23289811 0.15962050 0.21676965 0.24902658
[153,] 12549 12555 0.82357143 0.16277065 0.72237753 0.92476533
[154,] 12556 12678 0.54259350 0.90770922 0.40796982 0.67721717
[155,] 12679 12900 0.29581982 0.20082277 0.27364992 0.31798972
[156,] 12901 12906 0.90800000 0.11360282 0.83171472 0.98428528
[157,] 12907 13038 0.25925758 0.15798017 0.23664017 0.28187499
[158,] 13039 13056 2.66222222 0.35552042 2.52438848 2.80005597
[159,] 13057 13212 0.28707692 0.19893726 0.26087814 0.31327570
[160,] 13213 13423 0.26396209 0.23014310 0.23790151 0.29002266
[161,] 13424 13435 2.69416667 0.11325421 2.64039036 2.74794297
[162,] 13436 13473 0.10865789 0.02613507 0.10168426 0.11563153
[163,] 13474 13608 0.48853333 0.33009530 0.44180291 0.53526376
[164,] 13609 13624 2.92675000 0.37486984 2.77259849 3.08090151
[165,] 13625 13815 0.44051832 0.26973837 0.40841476 0.47262189
[166,] 13816 14006 0.38752880 0.17509618 0.36668930 0.40836829
 [ reached getOption("max.print") -- omitted 355 rows ]

plot(summary(bcp))

[1] "Change points"

[1] "Segments"
       begin   end       mean         SD   LL of CI   UL of CI
  [1,]     1   183 0.15867213 0.08151452 0.14876069 0.16858357
  [2,]   184   201 2.54833333 0.18683903 2.47589664 2.62077003
  [3,]   202   255 0.21370370 0.13612198 0.18323467 0.24417273
  [4,]   256   280 0.98676000 0.85564446 0.70527802 1.26824198
  [5,]   281   310 0.09933333 0.01595972 0.09454050 0.10412616
  [6,]   311   317 1.39928571 0.58181002 1.03757661 1.76099482
  [7,]   318   407 0.19588889 0.11159210 0.17654074 0.21523704
  [8,]   408   419 0.60500000 0.27843067 0.47279323 0.73720677
  [9,]   420   465 0.13995652 0.08194794 0.12008247 0.15983057
 [10,]   466   470 0.81600000 0.10968136 0.73531829 0.89668171
 [11,]   471   525 0.12314545 0.05444005 0.11107109 0.13521982
 [12,]   526   547 0.54072727 0.35733620 0.41541519 0.66603935
 [13,]   548   663 0.14043103 0.08869638 0.12688525 0.15397682
 [14,]   664   671 0.77875000 0.07972049 0.73238906 0.82511094
 [15,]   672   739 0.19141176 0.16167936 0.15916194 0.22366159
 [16,]   740   758 1.01136842 0.47855804 0.83078201 1.19195484
 [17,]   759   799 0.28295122 0.15253933 0.24376646 0.32213598
 [18,]   800   836 0.24978378 0.25941603 0.17963451 0.31993306
 [19,]   837   887 0.15256863 0.12176408 0.12452325 0.18061401
 [20,]   888   889 1.63700000 0.22203153 1.37875798 1.89524202
 [21,]   890  1004 0.30826957 0.26276249 0.26796616 0.34857297
 [22,]  1005  1276 0.17872059 0.12311218 0.16644213 0.19099905
 [23,]  1277  1313 0.14602703 0.17472510 0.09877922 0.19327484
 [24,]  1314  1333 0.83575000 0.64251405 0.59943304 1.07206696
 [25,]  1334  1353 2.52600000 0.15367087 2.46947978 2.58252022
 [26,]  1354  1411 0.15462069 0.19762234 0.11193824 0.19730314
 [27,]  1412  1420 0.81455556 0.32066654 0.63873905 0.99037206
 [28,]  1421  1470 0.16828000 0.06658432 0.15279133 0.18376867
 [29,]  1471  1559 0.31550562 0.25288347 0.27141434 0.35959690
 [30,]  1560  1564 1.17880000 0.19822386 1.03298635 1.32461365
 [31,]  1565  1614 0.14046000 0.07016459 0.12413849 0.15678151
 [32,]  1615  1624 1.20980000 0.08634530 1.16488763 1.25471237
 [33,]  1625  1686 0.18277419 0.14634021 0.15220422 0.21334417
 [34,]  1687  1698 3.15425000 0.49666051 2.91842151 3.39007849
 [35,]  1699  1854 0.15433974 0.06694095 0.14552404 0.16315544
 [36,]  1855  1861 0.84342857 0.21794790 0.70793118 0.97892596
 [37,]  1862  1908 0.09882979 0.08260654 0.07901028 0.11864929
 [38,]  1909  1926 2.59166667 0.16059632 2.52940416 2.65392917
 [39,]  1927  2101 0.15522286 0.17138946 0.13391242 0.17653329
 [40,]  2102  2200 0.17367677 0.17937310 0.14402388 0.20332965
 [41,]  2201  2218 2.46388889 0.34700634 2.32935602 2.59842176
 [42,]  2219  2264 0.18486957 0.07748623 0.16607757 0.20366156
 [43,]  2265  2476 0.19737264 0.16586218 0.17863535 0.21610993
 [44,]  2477  2479 0.75833333 0.45885219 0.32258128 1.19408538
 [45,]  2480  2495 2.76906250 0.35950326 2.62122994 2.91689506
 [46,]  2496  2572 0.13181818 0.05257044 0.12196393 0.14167243
 [47,]  2573  2594 0.47481818 0.25175189 0.38653283 0.56310353
 [48,]  2595  2677 0.14198795 0.09708150 0.12446026 0.15951564
 [49,]  2678  2683 0.84000000 0.21071308 0.69850434 0.98149566
 [50,]  2684  2759 0.13947368 0.06365333 0.12746372 0.15148364
 [51,]  2760  2779 3.07070000 0.77736005 2.78478662 3.35661338
 [52,]  2780  3060 0.12311388 0.06383092 0.11685055 0.12937721
 [53,]  3061  3081 2.59571429 0.44914999 2.43449797 2.75693061
 [54,]  3082  3147 0.13696970 0.06288108 0.12423832 0.14970107
 [55,]  3148  3172 0.24856000 0.18372082 0.18812123 0.30899877
 [56,]  3173  3524 0.09061648 0.04654004 0.08653627 0.09469669
 [57,]  3525  3638 0.20732456 0.17125908 0.18094130 0.23370782
 [58,]  3639  3640 1.27500000 0.37476659 0.83911379 1.71088621
 [59,]  3641  3732 0.17995652 0.12108910 0.15919121 0.20072183
 [60,]  3733  3751 2.60526316 0.13226104 2.55535375 2.65517256
 [61,]  3752  3878 0.09667717 0.04120196 0.09066344 0.10269089
 [62,]  3879  3895 0.56470588 0.29951435 0.44521894 0.68419282
 [63,]  3896  4020 0.17004800 0.12667222 0.15141196 0.18868404
 [64,]  4021  4034 2.63500000 0.12882606 2.57836734 2.69163266
 [65,]  4035  4201 0.20934132 0.15551561 0.18954689 0.22913575
 [66,]  4202  4218 3.13858824 0.60423765 2.89753633 3.37964014
 [67,]  4219  4290 0.16494444 0.10834763 0.14394149 0.18594740
 [68,]  4291  4312 0.59354545 0.35545045 0.46889468 0.71819623
 [69,]  4313  4404 0.16091304 0.09404718 0.14478510 0.17704099
 [70,]  4405  4608 0.25860784 0.20945656 0.23448624 0.28272945
 [71,]  4609  4623 2.61266667 0.45051505 2.42133320 2.80400013
 [72,]  4624  4697 0.17281081 0.12154662 0.14956984 0.19605178
 [73,]  4698  4704 0.91428571 0.11222851 0.84451367 0.98405776
 [74,]  4705  4787 0.12692771 0.11129425 0.10683396 0.14702146
 [75,]  4788  4793 0.77833333 0.16154463 0.66985471 0.88681195
 [76,]  4794  4888 0.12975789 0.07625402 0.11688939 0.14262640
 [77,]  4889  4907 2.49736842 0.43878421 2.33179087 2.66294597
 [78,]  4908  5214 0.13712378 0.08533592 0.12911272 0.14513484
 [79,]  5215  5228 2.41135714 0.49351387 2.19440567 2.62830862
 [80,]  5229  5362 0.23662687 0.18332709 0.21057724 0.26267649
 [81,]  5363  5377 2.77400000 0.34652973 2.62682904 2.92117096
 [82,]  5378  5464 0.21665517 0.22068165 0.17773867 0.25557168
 [83,]  5465  5651 0.17728342 0.11530101 0.16341460 0.19115224
 [84,]  5652  5668 2.57411765 0.26923639 2.46670966 2.68152563
 [85,]  5669  5741 0.13509589 0.08354526 0.11901213 0.15117965
 [86,]  5742  5748 1.17614286 0.12781683 1.09667961 1.25560610
 [87,]  5749  6096 0.17443103 0.15804380 0.16049577 0.18836630
 [88,]  6097  6112 2.60718750 0.45955823 2.41821100 2.79616400
 [89,]  6113  6124 0.29750000 0.20276027 0.20122368 0.39377632
 [90,]  6125  6131 2.76257143 0.97367703 2.15724009 3.36790277
 [91,]  6132  6231 0.24848000 0.25669557 0.20625734 0.29070266
 [92,]  6232  6308 0.21520779 0.12653378 0.19148923 0.23892636
 [93,]  6309  6511 0.19472414 0.17713057 0.17427511 0.21517317
 [94,]  6512  6525 2.48407143 0.27672159 2.36242306 2.60571980
 [95,]  6526  6671 0.14185616 0.08498146 0.13028772 0.15342461
 [96,]  6672  6673 0.92100000 0.22910260 0.65453371 1.18746629
 [97,]  6674  6879 0.27940291 0.23138322 0.25288584 0.30591998
 [98,]  6880  6900 2.72452381 0.68929207 2.47711173 2.97193589
 [99,]  6901  7075 0.21150857 0.14219340 0.19382835 0.22918879
[100,]  7076  7095 0.52810000 0.34191826 0.40234231 0.65385769
[101,]  7096  7153 0.19832759 0.12769857 0.17074727 0.22590791
[102,]  7154  7179 0.59700000 0.26742042 0.51073490 0.68326510
[103,]  7180  7280 0.25075248 0.19670305 0.21855827 0.28294668
[104,]  7281  7298 2.83583333 0.19686730 2.75950872 2.91215794
[105,]  7299  7550 0.24308730 0.18568744 0.22384710 0.26232750
[106,]  7551  7563 1.10323077 0.43770579 0.90354925 1.30291229
[107,]  7564  7581 2.75872222 0.75105877 2.46753995 3.04990449
[108,]  7582  7913 0.33763253 0.19658596 0.31988611 0.35537895
[109,]  7914  8044 0.38840458 0.28637675 0.34724894 0.42956022
[110,]  8045  8054 0.72430000 0.23386608 0.60265493 0.84594507
[111,]  8055  8253 0.29678392 0.22912889 0.27006736 0.32350048
[112,]  8254  8278 2.59120000 0.29066476 2.49557980 2.68682020
[113,]  8279  8542 0.35060985 0.24308582 0.32600136 0.37521834
[114,]  8543  8557 2.57466667 0.41889083 2.39676399 2.75256934
[115,]  8558  8620 0.61955556 0.44157080 0.52804792 0.71106319
[116,]  8621  8801 0.31927624 0.21471628 0.29302482 0.34552767
[117,]  8802  8819 0.44194444 0.27913743 0.33372406 0.55016483
[118,]  8820  8833 2.71357143 0.18002594 2.63443101 2.79271185
[119,]  8834  9006 0.38552023 0.36266991 0.34016622 0.43087424
[120,]  9007  9024 2.85411111 0.24699366 2.75835273 2.94986950
[121,]  9025  9120 0.50219792 0.39500832 0.43588504 0.56851080
[122,]  9121  9394 0.44655474 0.30289382 0.41645642 0.47665307
[123,]  9395  9416 2.67545455 0.27109526 2.58038578 2.77052331
[124,]  9417  9555 0.29738129 0.19514767 0.27015533 0.32460726
[125,]  9556  9636 0.41034568 0.31565975 0.35265522 0.46803613
[126,]  9637  9651 2.56346667 0.59115128 2.31240508 2.81452825
[127,]  9652  9872 0.33284615 0.26946999 0.30303069 0.36266161
[128,]  9873  9888 2.66875000 0.28710590 2.55068820 2.78681180
[129,]  9889 10028 0.36028571 0.25706952 0.32454907 0.39602236
[130,] 10029 10165 0.43079562 0.35885920 0.38036538 0.48122586
[131,] 10166 10230 0.22975385 0.22577014 0.18369245 0.27581524
[132,] 10231 10239 2.54944444 0.65648993 2.18950116 2.90938773
[133,] 10240 10251 0.39150000 0.11319693 0.33775089 0.44524911
[134,] 10252 10253 2.40100000 0.66326616 1.62956365 3.17243635
[135,] 10254 10268 0.36380000 0.22700824 0.26738974 0.46021026
[136,] 10269 10284 2.63562500 0.05303694 2.61381550 2.65743450
[137,] 10285 10342 0.18908621 0.15608166 0.15537571 0.22279670
[138,] 10343 10353 0.79100000 0.26893977 0.65762146 0.92437854
[139,] 10354 10454 0.26540594 0.17848794 0.23619299 0.29461889
[140,] 10455 10469 2.47533333 0.51804394 2.25532039 2.69534627
[141,] 10470 10556 0.21736782 0.17195937 0.18704333 0.24769230
[142,] 10557 10564 0.83000000 0.09471763 0.77491757 0.88508243
[143,] 10565 10733 0.23657396 0.16448399 0.21576227 0.25738566
[144,] 10734 10751 2.71350000 0.26413148 2.61109735 2.81590265
[145,] 10752 11007 0.24485938 0.19501390 0.22481129 0.26490746
[146,] 11008 11099 0.34401087 0.26032564 0.29936819 0.38865355
[147,] 11100 11113 0.62950000 0.35320854 0.47422754 0.78477246
[148,] 11114 11134 2.76833333 1.03796514 2.39576977 3.14089690
[149,] 11135 11475 0.19822287 0.15181241 0.18470036 0.21174539
[150,] 11476 11814 0.22865192 0.16220743 0.21416092 0.24314291
[151,] 11815 12283 0.25126013 0.25484719 0.23190390 0.27061635
[152,] 12284 12548 0.23289811 0.15962050 0.21676965 0.24902658
[153,] 12549 12555 0.82357143 0.16277065 0.72237753 0.92476533
[154,] 12556 12678 0.54259350 0.90770922 0.40796982 0.67721717
[155,] 12679 12900 0.29581982 0.20082277 0.27364992 0.31798972
[156,] 12901 12906 0.90800000 0.11360282 0.83171472 0.98428528
[157,] 12907 13038 0.25925758 0.15798017 0.23664017 0.28187499
[158,] 13039 13056 2.66222222 0.35552042 2.52438848 2.80005597
[159,] 13057 13212 0.28707692 0.19893726 0.26087814 0.31327570
[160,] 13213 13423 0.26396209 0.23014310 0.23790151 0.29002266
[161,] 13424 13435 2.69416667 0.11325421 2.64039036 2.74794297
[162,] 13436 13473 0.10865789 0.02613507 0.10168426 0.11563153
[163,] 13474 13608 0.48853333 0.33009530 0.44180291 0.53526376
[164,] 13609 13624 2.92675000 0.37486984 2.77259849 3.08090151
[165,] 13625 13815 0.44051832 0.26973837 0.40841476 0.47262189
[166,] 13816 14006 0.38752880 0.17509618 0.36668930 0.40836829
 [ reached getOption("max.print") -- omitted 355 rows ]

onlineBcp model seems to change efficacy when looking across different lengths of time. It works well on single days, but when looking at a week, it picks up noise from HVAC/other appliances, though still picking up the EV charging pretty well. When looking at longer periods, however, the EV charging intervals get lost in the noise.

Also, the original bcp package for R was deprecated, leaving the best option as onlineBCP. Maybe build a training data set with manually labeled EV charging intervals, then run a machine learning model on the rest of the data set? Use time series inputs.

Identify streaks of high electricity usage (2 kWh in 15 minute interval and 2 kWh in previous and/or next interval)

sample <- sample %>% 
  mutate(next_net = lead(consumed_kWh, order_by = datetime), prev_net = lag(consumed_kWh, order_by = datetime)) %>% 
  #mutate(next2 = lead(consumed_kWh, order_by = datetime, n = 2L), next3 = lead(consumed_kWh, order_by = datetime, n = 3L)) %>% 
  #mutate(nexthour_net = consumed_kWh + next_net + next2 + next3) %>% 
  mutate(streak = case_when(consumed_kWh > 2 & (next_net > 2 | prev_net > 2) ~ 1)) %>% 
  #mutate(streak_net = ) %>% 
  #mutate(start_of_ev_charge = case_when(streak == 1 & is.na(lag(streak, order_by = datetime)) ~ datetime), #& nexthour_net >= 10000
  #       end_of_ev_charge = case_when(streak == 1 & is.na(lead(streak, order_by = datetime)) ~ end), 
  #       ev_charge_time = 0, ev_charge_net = 0, ev_charge_min = 0, ev_charge_max = 0, customer_id = customer_id) %>% 
  arrange(datetime)

sample

Now, look at length of streaks to identify charging sessions. (not finished)

This doesn’t work yet: